title: Relation between brain architecture and mathematical ability in children: a DBM study.
abstract: Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children.
authors.affiliation: Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA. Electronic address: Natalie.hanz@gmail.com.
authors.forename: Zhaoying
authors.initials: Z
authors.lastname: Han
authors.forename: Nicole
authors.initials: N
authors.lastname: Davis
authors.forename: Lynn
authors.initials: L
authors.lastname: Fuchs
authors.forename: Adam W
authors.initials: AW
authors.lastname: Anderson
authors.forename: John C
authors.initials: JC
authors.lastname: Gore
authors.forename: Benoit M
authors.initials: BM
authors.lastname: Dawant
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D002648:Child; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008433:Mathematics; D059906:Neuroimaging / Q000379:methods*; D009929:Organ Size; D011340:Problem Solving / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013647:Task Performance and Analysis
keywords: Brain atlas; Deformation based morphometry; Mathematical ability; Non-rigid registration